The Influencing Factors of Students' Acceptance of Artificial Intelligence Usage at Pingdingshan Vocational College
Article Sidebar
Main Article Content
บทคัดย่อ
This study investigates the factors influencing students' acceptance of artificial intelligence (AI) at Pingdingshan Vocational College, utilizing the Unified Theory of Acceptance and Use of Technology (UTAUT) as the theoretical framework
A quantitative empirical approach was employed, utilizing structured questionnaires administered to students via random sampling. From a total of 400 distributed surveys, 377 valid responses were collected, achieving a 94.25% effective response rate. The data were evaluated using multiple linear regression analysis
Based on these discoveries, students generally exhibited a high level of AI acceptance, with average scores for all core variables exceeding 4.1 out of 5. The empirical model demonstrated strong explanatory power (adjusted R² = 0.749, p < 0.001). The results confirmed that all four core factors significantly and positively impact students' acceptance of AI. Specifically, social influence (Beta = 0.285) and facilitating conditions (Beta = 0.247) exerted the strongest standardized impacts on acceptance, followed by effort expectancy (Beta = 0.067) and performance expectancy (Beta = 0.048).
AI acceptance among vocational students is heavily shaped by social dynamics and institutional support, alongside the technology's perceived ease of use and academic benefits. Based on these findings, the study recommends cultivating students' internal drive, establishing a comprehensive school technical support system, and promoting the intelligent upgrading of the school's management infrastructure to optimize AI adoption.
Article Details
เอกสารอ้างอิง
Chen, X. (2024). Exploration of secondary vocational mathematics teaching strategies under the background of core competencies. Road to Success, 13(22), 81–84.
Fang, X., Zhang, X. H., & Li, L. (2018). Influencing factors of teachers' behavioral intention on the STEM online education platform: A survey based on the Wise platform of South China Normal University. Open Education Research, 17(3), 59–67.
Feng, J. B., Zhang, G. L., Jin, S. Z. (2022). Research on countermeasures for improving the virtual experimental teaching ability of primary and secondary school teachers: Based on technology acceptance assessment. China Educational Technology, 3(7), 120–126.
Jiang, L. P., Xie, C. B., Jia, Q. S. (2023). Empowerment, transformation, and reform: Analysis of the “digitalization” theme of the winning achievements of the 2022 National Teaching Achievement Awards in vocational education. China Vocational and Technical Education, 9(26), 65–74.
Li, H. X., Zhao, C. L., Jiang, Z. H. (2017). Influencing factors of preschool teachers' acceptance of information-based teaching: An empirical analysis based on the UTAUT model. Research in Preschool Education, 5(4), 14–25.
Li, R. J., & Cheng, J. J. (2025). Influencing factors of college students' digital and intelligent technology learning behavior: Also discussing the moderating effect of overconfidence. Continuing Education Research, 15(2), 106–112.
Li, Z. W., Ma, X. L., Gao, X. (2022). Structural analysis of specialized code preference, technology acceptance, and online English learning engagement. Foreign Languages Research, 24(5), 63–70.
Ma, M. Q. (2024). Research on the acceptance and influencing factors of pre-service English teachers toward generative artificial intelligence: A qualitative analysis based on the UTAUT model. Foreign Language Education in China, 3(4), 67–73.
Muhamad, W., Suhardi, K., & Bandung, Y. (2025). Evaluation of technology integration’s influence on the virtual classroom service system acceptance using the UTAUT framework. Journal of Computers in Education, 3(11), 1–29.
Venkatesh D., Davis G. B, Viswanath R. (2003). User acceptance of information technology: Toward a unified view. MIS Quarterly, 2(4), 425 - 478.
Venkatesh, V., Thong, J. Y. L., & Xu, X. (2012). Consumer acceptance and use of information technology: Extending the Unified Theory of Acceptance and Use of Technology. MIS Quarterly, 2(1), 157–178.
Wan, L. Y., & Zhao, C. L. (2016). Research on the influencing factors of information technology adoption and use among primary and secondary school students in ethnic minority areas based on the UTAUT model. Modern Distance Education, 17(2), 70–75.
Wang, Q. (2025). EFL learners’ motivation and acceptance of using large language models in English academic writing: An extension of the UTAUT model. Frontiers in Psychology, 15(10), 15-18.
Wang, Y. B., Wan, K., & Ren, Y. Q. (2019). Research on the influencing factors of primary and secondary school teachers' acceptance of robot education. E-Education Research, 17(6), 105–111.
Zhang, H. Y., Qian, D. M., & Ren, Y. Q. (2015). Promoting the teaching application of electronic schoolbags: An empirical study on student acceptance. Education Research, 6(10), 92–97.
